Deep learning for NBR. Deep learning [1]}, [2]}, [3]}, [4]}, [5]} gains momentum in RS given its potential in learning powerful latent representations of users/items [6]}. Its ability to model sequential patterns is especially interesting for NBR. Many new methods based on recurrent neural networks and attention mechanisms [7]} have been proposed [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}. However, questions for the effectiveness of deep learning in the RS domain, especially next basket recommendation/sequential recommendation, remain open. Other studies show that simple neighborhood-based methods outperform complex neural models easily [15]}, , [16]}.
